(534f) Novel Synthetic Inducible Biological Regulators
We are designing, constructing and characterizing novel synthetic gene regulators. These regulators range from individual inducible fusion protein and promoter pairs, to complex nonlinear regulatory networks. All of the regulators are designed to be compatible in E. coli and to control gene expression by direct transcription activation. By integrating current molecular biology tools with an engineering approach we are building these complex and robust synthetic regulatory networks while reducing the required expense and effort for constructing such networks. Our investigations have complementary experimental and computer model components that allow us to do this.
To date, we have designed an initial two part synthetic system composed of an inducible fusion regulatory protein and a complementary fusion promoter. The fusion protein is a novel combination of an inducible DNA binding domain, reverse TetR, and a transcription activation domain, LuxRΔN(2-162). The fusion promoter contains the complementary operator sequences, tetO and luxbox, for both protein domains. The system is designed to be simple and adjustable, enabling us to optimize the experimental system and to accurately model the experimental phenotypes. The system has been experimentally constructed and its gene expression regulation is being characterized by flow cytometry.
Additionally, the simple and adjustable nature of the system is allowing us to construct system variants. These variants also contain unique inducible DNA binding domains and transcription activation domains such that they are inducible in a variety of environmental conditions. The variants are designed and combined into complex, nonlinear, gene regulatory networks. These networks include switches, oscillators, auto regulators, positive feedback loops, and logic gates. The networks have also been designed to display logical behavior, to be NOT, AND, OR, and NOR inducible.
As each synthetic regulatory is constructed and tested experimentally, its gene expression control will also be stochastically modeled using the Synthetic Biology Software Suite (Hill et al. 2008). By characterizing the experimental systems and investigating them with stochastic computer models, we will be able to rationally design future complex networks to display a desired phenotype prior to constructing them experimentally.